Composite Learning Units: Generalized Learning Beyond Parameter Updates
to Transform LLMs into Adaptive Reasoners
Composite Learning Units: Generalized Learning Beyond Parameter Updates
to Transform LLMs into Adaptive Reasoners
Human learning thrives on the ability to learn from mistakes, adapt through feedback, and refine understanding-processes often missing in static machine learning models. In this work, we introduce Composite Learning Units (CLUs) designed to transform reasoners, such as Large Language Models (LLMs), into learners capable of generalized, continuous learning without …